For an Empirical Reading of Physics

This essay invites the reader to interpret physics from a radically empirical standpoint, both diachronic and relative. We start with some criteria of the theory of knowledge, the basis for interpreting the fundamentals of mathematics and physics. Then we present some expositions of physics, including a new characterization of time, space and movement, with reference to classical mechanics, relativity and quantum mechanics

Novel application of stochastic modeling techniques to long-term, high-resolution time-lapse microscopy of cortical axons

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 64-70).Axons exhibit a rich variety of behaviors, such as elongation, turning, branching, and fasciculation, all in service of the complex goal of wiring up the brain. In order to quantify these behaviors, I have developed a system for in vitro imaging of axon growth cones with time-lapse fluorescence microscopy. Image tiles are automatically captured and assembled into a mosaic image of a square millimeter region. GFP-expressing mouse cortical neurons can be imaged once every few minutes for up to weeks if phototoxicity is minimized. Looking at the data, the trajectories of axon growth cones seem to alternate between long, straight segments and sudden turns. I first rigorously test the idea that the straight segments are generated from a biased random walk by analyzing the correlation between growth cone steps in the time and frequency domain. To formalize and test the intuition that sharp turns join straight segments, I fit a hidden Markov model to time series of growth cone velocity vectors.(cont.) The hidden state variable represents the bias direction of a biased random walk, and specifies the mean and variance of a Gaussian distribution from which velocities are drawn. Rotational symmetry is used to constrain the transition probabilities of the hidden variable, as well as the Gaussian distributions for the hidden states. Maximum likelihood estimation of the model parameters shows that the most probable behavior is to remain in the same hidden state. The second most probable behavior is to turn by about 40 degrees. Smaller angle turns are highly improbable, consistent with the idea that the axon makes sudden turns. When the same hidden Markov model was applied to artificially generated meandering trajectories, the transition probabilities were significant only for small angle turns. This novel application of stochastic models to growth cone trajectories provides a quantitative framework for testing interventions (eg. pharmacological, activity-related, etc.) that can impact axonal growth cone movement and turning. For example, manipulations that inhibit actin polymerization increase the frequency and angle of turns made by the growth cone. More generally, axon behaviors may be useful in deducing computational principles for wiring up circuits.by Neville Espi Sanjana.Ph.D

Complex shock structure in the western hot-spot of Pictor A

We have carried out simulations of supersonic light jets in order to model the features observed in optical and radio images of the western hot-spot in the radio galaxy Pictor A. We have considered jets with density ratios η =10[superscript −2] − 10[superscript −4], and Mach numbers ranging between 5 and 50. From each simulation, we have generated raytraced maps of radio surface brightness at a variety of jet inclinations, in order to study the appearance of time-dependent luminous structures in the vicinity of the western hotspot. We compare these rendered images with observed features of Pictor A. A remarkable feature of Pictor A observations is a bar-shaped “filament” inclined almost at right angles to the inferred jet direction and extending 24" (10.8h[superscript −1] kpc) along its longest axis. The constraints of reproducing the appearance of this structure in simulations indicate that the jet of Pictor A lies nearly in the plane of the sky. The results of the simulation are also consistent with other features found in the radio image of Pictor A. This filament arises from the surging behaviour of the jet near the hot-spot; the surging is provoked by alternate compression and decompression of the jet by the turbulent backflow in the cocoon. We also examine the arguments for the jet in Pictor A being at a more acute angle to the line of sight and find that our preferred orientation is just consistent with the limits on the brightness ratio of the X-ray jet and counter-jet. We determine from our simulations, the structure function of hot-spot brightness and also the cumulative distribution of the ratio of intrinsic hot-spot brightnesses. The latter may be used to quantify the use of hot-spot ratios for the estimation of relativistic effects

Novel analysis and modelling methodologies applied to pultrusion and other processes

Often a manufacturing process may be a bottleneck or critical to a business. This thesis focuses on the analysis and modelling of such processest, to both better understand them, and to support the enhancement of quality or output capability of the process. The main thrusts of this thesis therefore are: To model inter-process physics, inter-relationships, and complex processes in a manner that enables re-exploitation, re-interpretation and reuse of this knowledge and generic elements e.g. using Object Oriented (00) & Qualitative Modelling (QM) techniques. This involves the development of superior process models to capture process complexity and reuse any generic elements; To demonstrate advanced modelling and simulation techniques (e.g. Artificial Neural Networks(ANN), Rule-Based-Systems (RBS), and statistical modelling) on a number of complex manufacturing case studies; To gain a better understanding of the physics and process inter-relationships exhibited in a number of complex manufacturing processes (e.g. pultrusion, bioprocess, and logistics) using analysis and modelling. To these ends, both a novel Object Oriented Qualitative (Problem) Analysis (OOQA) methodology, and a novel Artificial Neural Network Process Modelling (ANNPM) methodology were developed and applied to a number of complex manufacturing case studies- thermoset and thermoplastic pultrusion, bioprocess reactor, and a logistics supply chain. It has been shown that these methodologies and the models developed support capture of complex process inter-relationships, enable reuse of generic elements, support effective variable selection for ANN models, and perform well as a predictor of process properties. In particular the ANN pultrusion models, using laboratory data from IKV, Aachen and Pera, Melton Mowbray, predicted product properties very well

From A to B, statistical modelling of the ecology of ants and badgers

Biological systems involve features/behaviours of individuals and populations that are influenced by a multitude of factors. To explore the dynamics of such systems, a statistical description offers the possibility of testing hypotheses, drawing predictions and more generally, assessing our understanding. In the work presented, I analyse the properties of various biological systems of two very different organisms: Pharaoh‟s ants (Monomorium pharaonis) and badgers (Meles meles). The basis of the work, in the two projects on these biological systems, relies heavily on data collection and explaining observations using quantitative methods such as statistical analysis and simulations. In the first part of this thesis, I describe animal movement in space and time using data collected on the foraging behaviour of ants. A new model is presented which appears to reflect, with a high degree of accuracy, the behaviour of real organisms. This model constitutes the basis of the second chapter in which the qualities of searching strategies are explored in the context of optimal foraging. The final chapter of first part of this thesis concludes with a detailed analysis of the rate of exploration of individuals. As an essential part of foraging, the rate of individuals leaving their nest is analysed using collected data, and contrasted with results derived from a mathematical model. The second part of this thesis focuses on badgers. A first chapter explores the significance of palate maculation that is observed in badgers and relates their symmetry to parasitic infection. I then explore the population dynamics of a population of badgers subject to natural variation in climatic conditions. A first analysis is based on local climatic conditions, while a second analysis focuses on a more general property of climate (i.e. its unpredictability) to infer population dynamics

Mechanoelectrical signalling via ELKIN1 and PIEZO1 in cell lines derived from metastatic melanoma

Mechanoelectrical transduction is mediated by mechanically activated (MA) ion channels, which are proteins embedded in the plasma cell membrane that open in response to a physical stimulus. The discovery of MA ion channels in mammalian cancer cells has raised questions regarding the roles of these molecules in cell migration and tumour invasion. There is a lack of a clear consensus regarding the role of mechanoelectrical transduction in cell migration and invasion, particularly since there have been major discrepancies in the types of experimental approaches used to study the impact of these molecules on cellular function. Using metastatic melanoma as a model, this thesis aims to investigate the roles of ELKIN1, a novel candidate MA ion channel, and PIEZO1, a well-characterised MA ion channel, in cell migration and dissociation from cell clusters. The first results chapter of this thesis reports the generation of gene edited secondary melanoma cell lines, WM266-4 and A375-MA2, using CRISPR/Cas9 technology to create knockout clones. In ELKIN1 and PIEZO1 knockout clones, transcript levels of each respective molecule were undetected, and PIEZO1 knockout clones were confirmed to lack PIEZO1 activity using the chemical activator, Yoda1. The second results chapter investigates the differential effects on cell migration on two-dimensional (2D) and three-dimensional (3D) environments by utilising knockout clones in both WM266-4 and A375-MA2, as characterised in the first results chapter. The third results chapter reports the role of ELKIN1 in modulating cell-cell interactions in WM266-4, whereby the knockout of ELKIN1 resulted in increased cellular dissociation and partitioning to the outer layer of organotypic spheroids. The final results chapter reports the effects of substrate stiffness on cellular and nuclear morphological parameters when mechanoelectrical transduction via ELKIN1 and PIEZO1 is disrupted in WM266-4 and A375-MA2 cell lines. Given the lack of mechanistic understanding of mechanoelectrical transduction in cell migration and invasion, this fundamental research provides a systematic in vitro approach to delineating the functional roles of ELKIN1 and PIEZO1 in the context of metastatic melanoma. Differential effects of ELKIN1 and PIEZO1 on cell behaviour were found to depend on the cell background, and the dimensionality, mechanics and matrix composition of the microenvironment. These findings suggest a diverse set of roles for mechanoelectrical transduction in regulating cell behaviour

Novel descriptive and model based statistical approaches in immunology and signal transduction

Biological systems are usually complex nonlinear systems of which we only have a limited understanding. Here we show three different aspects of investigating such systems. We present a method to extract detailed knowledge from typical biological trajectory data, which have randomness as a main characteristic. The migration of immune cells, such as leukocytes, are a key example of our study. The application of our methodology leads to the discovery of novel random walk behaviour of leukocyte migration. Furthermore we use the gathered knowledge to construct the under- lying mathematical model that captures the behaviour of leukocytes, or more precisely macrophages and neutrophils, under acute injury. Any model of a biological system has little predictive power if it is not compared to collected data. We present a pipeline of how complex spatio- temporal trajectory data can be used to calibrate our model of leukocyte migration. The pipeline employs approximate methods in a Bayesian framework. Using the same approach we are able to learn additional information about the underlying signalling network, which is not directly apparent in the cell migration data. While these two methods can be seen as data processing and analysis, we show in the last part of this work how to assess the information content of experiments. The choice of an experiment with the highest information content out of a set of possible experiments leads us to the problem of optimal experimental design. We develop and implement an algorithm for simulation based Bayesian experimental design in order to learn parameters of a given model. We validate our algorithm with the help of toy examples and apply it to examples in immunology (Hes1 transcription regulation) and signal transduction (growth factor induced MAPK pathway)